We now describe the settings of the neural networks. As for the function NetInference, the dimension of embedding vectors is set to 256, and the Siamese neural network model is trained via the Adam optimizer [1]} with a learning rate of 0.1 and a batch size of 16 for all datasets. For computational efficiency, our model is trained just for a few epochs from the second round of queries based on the model parameters \({\bf W}\)  loaded from the previous query round, which is in line with a popular pre-training technique in the literature [2]}.
